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Computer requirements - PIX4Dfields

This article outlines the minimum and recommended hardware and software specifications necessary to achieve optimal performance with PIX4Dfields. It also provides a comparison of processing times between different machines.

IN THIS ARTICLE

Operating system

Minimum specifications

Recommended specifications 

Processing times comparison in PIX4Dfields 2.9

Operating system

  • Windows_logo_-_2012 Windows: 
    • PIX4Dfields 1.12 and newer versions: Windows 10 (64 Bit) and Windows 11 (64 Bit) supported.
    • PIX4Dfields 1.11 and previous versions: Windows 10 (64 Bit) supported.
  • Apple_logo_black.svg macOS:
    • PIX4Dfields 2.9: Sonoma (14) and Sequoia (15). Native Apple silicon support.
      Intel processors are not supported.
    • PIX4Dfields 2.8: Ventura (13.0) or above. All chips.

Earlier versions of PIX4Dfields are compatible with older macOS versions; however, please note that Pix4D does not offer support for any issues that may arise.

    • PIX4Dfields 2.7: Ventura (13.0) or above. All chips. 
    • PIX4Dfields 2.4: Monterey (12.0) or above.
    • PIX4Dfields 2.3.1 and previous versions: Big Sur (11.0).

Apple devices with Intel processors should continue using PIX4Dfields version 2.8 or previous, and avoid updates.


Minimum specs

  Windows_logo_-_2012 Windows Apple_logo_black.svg macOS
CPU
  • Quad-core or hexa-core Intel i5 (or better).
  • AMD Phenom processor (or better).
  • Apple M1 (or better).
GPU
  • Any (NVIDIA/Intel/AMD) GPU with 2 GB RAM..
  • Support for OpenGL 4.1 or higher.
  • Apple Integrated GPU 
RAM
  • 16 GB RAM (or more).
  • 16 GB RAM (or more).
Disk 
  • HDD 4GB free space (more for large datasets).
  • SSD 50 GB - 100 GB free space (with 4x dataset size free space.)
  • HDD 4GB free space (more for large datasets).
  • SSD 50 GB - 100 GB free space.
Display 1024×768 display resolution (or higher).

Recommended specs

  Windows_logo_-_2012 Windows Apple_logo_black.svg macOS
CPU
  • 8 cores or more:
    • Intel i7, i9 (or faster).
    • AMD Ryzen (or faster).
GPU
  • GeForce GTX or RTX, with 6GB RAM or better.
  • Apple Integrated GPU 
RAM
  • 32 GB RAM (or more)
  • 24 GB RAM (or more).
Disk 
  • SSD NVME with 4x dataset size free space.
  • SSD NVME with 4x dataset size free space.
Display 1024×768 display resolution (or higher).

The hierarchy of components that significantly influence the performance of PIX4Dfields is as follows:
1 - CPU Cores.
2 - Fast SSD (NVME).
3 - RAM.
4 - GPU (Nvidia preferred).

Sign up for a 15-day trial license to test PIX4Dfields using our Example use cases.

Processing times comparison in PIX4Dfields 2.9

The five machines underwent testing by processing five RGB and five Multispectral (MSP) datasets captured by various cameras and drones.

The processing times are derived from the average of all times recorded for each machine across different datasets and processing pipelines.

Machine Specifications
Mac  M1 Macbook M1 Pro - 32GB RAM - OS Sequoia 15.2
Mac  M4 Mac mini M4 - 24GB RAM - OS Sequoia 15.2
Windows i7  i7 10750H 6 cores  - 16GB RAM -  RTX 1650Ti - SSD NVME
Windows i9 i9 13900H 14 cores - 64GB RAM - RTX 4070 - SSD NVME
Windows AMD Ryzen Ryzen 7 3700X 8 cores - 32 GB RAM - RTX 3080


The results were as follows:

Fast processing pipeline

MacBooks (M1 and M4) deliver similar total processing times for the fast processing pipeline,  beating the Windows options.

Fast_3

  • The data show that in the Fast processing pipeline, even a modest Mac M1 or a mid-range Windows CPU/GPU can produce results in just a few minutes.
  • For example, macOS M1 averages ~5 minutes (MSP + RGB). That’s very fast compared to the high-end i9 system (~7 minutes) or AMD (~7minutes).
  • This implies that an expensive, top-of-the-line machine is unnecessary to run the fast pipeline quickly. A cheaper i7 (or an M1 Mac) could suffice if fast processing pipeline is used. 
💡The fast processing pipeline is ideal for a wide range of work scenarios, as it achieves geolocation accuracy comparable to that of the Accurate processing pipeline, while significantly reducing processing time. It is recommended to use Accurate processing pipeline only when a detailed DSM is needed. 


Accurate processing pipeline 

The Mac M4 was the fastest processor for the accurate processing pipeline, followed very closely by the Windows i9 machine and Mac M1.

Accureate_3

  • When Accurate processing is needed due to the need for a detailed Digital Surface Model, differences in machine capacity are more remarkable.
  • In such scenarios, utilizing a more powerful machine can significantly reduce processing times, potentially saving you tens of minutes or even hours on larger projects. If you frequently undertake substantial accurate tasks, investing in a mid-range to high-end system could prove beneficial.

Datasets used for the comparison

Some example datasets used for these tests can be found here: Example use cases - PIX4Dfields.

Dataset/camera Sensor Images Hectare GSD [cm/px] Size GB Average processing time (minutes)
Fast Accurate
1- Altum 3.2 MP per band 1404 8.4 2.8 6.97 1.1 5.5
2- M3M - MSP  2 MP per band 3092 22 3 27.2 5.3 23.6
3- M3M - RGB  20 MP 775 24 1.8 4.9 2.7 22.1
4- M3M - RGB  20 MP 1686 38 1 16.6 6.9 51.8
5- M3M - MSP  2 MP per band 3212 42 3.6 30.2 5.3 21.1
6- Sequoia - MSP  1.2 MP per band 1724 88 10.4 3.95 1.1 8.4
7- S.O.D.A.  - RGB  20 MP 374 92 2.7 3.15 1.3 11.3
8- Phantom 4 - RGB 12 MP 1655 250 3.4 11.9 6.7 49.5

What is the maximum number of images that  PIX4Dfields can process?

PIX4Dfields does not limit the number of input images or the size of the generated orthomosaic. The maximum is determined by hardware limitations (disk space, memory size, operating system maximum file size) and time limitations (the time to compute an orthomosaic increases quickly beyond an input dataset size of several thousand images). Projects of more than 20.000 images were processed using PIX4Dfields. 

Important: The information presented in this article is based on Pix4D's testing experiences and should be regarded as a general reference rather than a definitive assurance.